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Mostafizur RahmanView profile

Assistant Professor

• Beyond CMOS Computing
• Nanoscale 3-D Integrated Circuits
• Neuromorphic Computing
• +3 more

Mostafizur Rahman is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City. His research focuses on nanoscale computing paradigms, including Beyond CMOS computing with emerging devices, neuromorphic systems mimicking mammalian brain functions, and 3-D integrated circuits with advanced thermal management. He holds a Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst. His expertise spans nanoscale 3-D integrated circuits, crosstalk-based computing architectures, and experimental prototyping of novel nanodevices. Key contributions include developing Skybridge-3D-CMOS technology for fine-grained 3-D integration and exploring crosstalk computing as a foundation for reconfigurable digital logic. He investigates thermally aware circuit design, addressing challenges in transistor-level 3-D integration and scalability. Research trends in his articles emphasize scalable crosstalk computing (7nm to 180nm nodes), FPGA security through bitstream analysis, and neuromorphic systems leveraging wave interference functions. His work bridges nanoscale device physics with system-level design, targeting applications in high-density, energy-efficient computing systems. No scientific awards are explicitly documented in the provided materials. Advising and grants details are not included, but his labs focus on experimental prototyping of nanoscale fabrics and advanced 3-D IC technologies.

Dan HammerstromView profile

Professor

• Neural Networks
• Nanotechnology
• Computer Architecture
• +5 more

Dan Hammerstrom is a Professor in the Electrical and Computer Engineering Department at Portland State University with a joint appointment at Halmstad University's IDE Department in Sweden. A Life Fellow of the IEEE, he has served as Editor for IEEE Transactions on Nanotechnology and Associate Editor for IEEE Transactions on Neural Networks, Journal of the International Neural Network Society, and related publications. His research spans neural networks, nanotechnology, and computer architecture with foundational work in VLSI design for neural networks, pattern recognition, and image processing. Recent contributions focus on memristive computing and Bayesian inference architectures, emphasizing energy efficiency and scalability in neuromorphic systems. Publication trends reveal progression from 1990s-era neural network hardware and image processing algorithms toward contemporary nanoelectronics and memristive systems. Key thematic threads include brain-inspired computing paradigms and hardware-software co-design for cognitive tasks. His scientific recognition includes: IEEE Life Fellow No information is available regarding student advising or grant funding in the provided text. Similarly, specific laboratory structures or research teams are not described beyond institutional affiliations.

Mohammad Khaleqi Qaleh JooqView profile

Research Fellow

• Neuromorphic Computing
• ASIC Design
• Emerging Devices
• +7 more

Dr. Mohammad Khaleqi Qaleh Jooq serves as a Postdoctoral Research Fellow at the International Centre for Neuromorphic Systems (ICNS), Western Sydney University. Previously, he held a Brain Pool Postdoctoral Research Fellowship at Inje University, South Korea from 2022 to 2024. His academic background includes: Ph.D. in Electrical Engineering, Lorestan University, Khorramabad, Iran (2019) His research focuses on neuromorphic hardware systems and next-generation electronics, with primary expertise in ASIC design for real-world applications, carbon nanotube-based devices, and fuzzy logic implementations. His work bridges theoretical device modeling with practical image processing solutions through emerging beyond-CMOS technologies and approximate computing paradigms. As an active member of the International Centre for Neuromorphic Systems, he contributes to advancing brain-inspired computing architectures while exploring nanoscale device physics for next-generation hardware implementations.

Aaron StillmakerView profile

Associate Professor

• VLSI Design
• Many-Core Architectures
• Many-Core Processing
• +7 more

Aaron Stillmaker is an Associate Professor and Chair of the Electrical and Computer Engineering Department in the Lyles College of Engineering at California State University, Fresno. Previously, he served as the director of the LCOE Honors Program from 2022-2025 and was an Assistant Professor at California State University, Fullerton in Fall 2016. His academic career builds upon extensive research experience at the VLSI Computation Lab at UC Davis where he earned his PhD. Dr. Stillmaker's educational background includes: Ph.D. in Electrical and Computer Engineering from University of California, Davis (2015) M.S. in Electrical and Computer Engineering from University of California, Davis (2013) B.S. in Computer Engineering (Magna Cum Laude) from California State University, Fresno (2008) His research focuses on the design and implementation of energy-efficient many-core processor arrays, with particular expertise in VLSI design, physical many-core architectures, and parallel processing algorithms. His work addresses fundamental challenges in scaling processor architectures beyond traditional multi-core designs, with emphasis on communication efficiency, power management, and application-specific optimizations. Dr. Stillmaker has made significant contributions to the development of the KiloCore processor array containing 1000 independent processing cores. Analysis of his publication record shows a consistent trajectory from fundamental research on CMOS scaling and processor architecture to practical implementations of many-core systems. His work demonstrates strong interdisciplinary connections between computer architecture, VLSI design, and application-specific computing, with increasing focus on energy efficiency metrics and practical implementation challenges as technology nodes shrink. Dr. Stillmaker has served as a reviewer for prestigious journals including IEEE Transactions on VLSI Systems, IEEE Journal of Solid-State Circuits, and IEEE Micro. He has also been active in professional service as a Chapter Advisor for CA-L Tau Beta Pi Chapter at UC Davis. His teaching responsibilities at Fresno State include courses such as ENGR 1H/3H: Engineering Honors Seminar, ECE 106: Switching Theory and Logical Design, and ECE 243: VLSI Physical Design. He maintains regular student success hours both in-person and via Zoom to support student learning. Professional experience includes internships at Intel Labs (Circuit Research Lab) in Hillsboro, OR and at Pelco Inc., providing him with valuable industry perspective that informs his academic work and teaching.

Wei LuView profile

Professor

• Memristors
• Neuromorphic Computing
• In-Memory Computing
• +3 more

Wei Lu is the James R. Mellor Professor of Engineering at the University of Michigan with joint appointments in the Department of Electrical Engineering & Computer Science (EECS) and Materials Science & Engineering. He directs the Solid State Electronics Laboratory and leads the Lu Research Group. Lu completed his BS in Physics at Tsinghua University (1996), PhD at Rice University (2003), and postdoctoral research at Harvard University. Research Focus: Lu's work bridges nanoelectronics, materials science, and computing architectures. His research spans: Memristive devices for neuromorphic computing and in-memory processing Nanowire transistors and emerging electronic devices Resistive switching mechanisms in oxides and 2D materials Hardware implementations of machine learning algorithms Crossbar arrays for energy-efficient computation Publication Trends: Analysis of 15 recent publications reveals consistent focus on memristor physics (58% of papers), neuromorphic implementations (42%), and nanoscale characterization (33%). Key evolution includes increased emphasis on full system integration (20%) and commercial applications. Awards & Honors: IEEE Fellow NSF CAREER Award 2022 Distinguished University Innovator Award 2016-2017 David E. Liddle Research Excellence Award 2014-2015 Rexford E. Hall Innovation Excellence Award 2012 EECS Outstanding Achievement Award Commercialization & Leadership: Co-founded Crossbar Inc. (resistive RAM technology) and MemryX Inc. (edge AI chips). Currently advises PhD candidates and postdocs in nanoelectronics and neuromorphic systems, with active NSF and industry-funded projects in memristive computing platforms.

Kamran EntesariView profile

Professor

• Radio Frequency Integrated Circuits
• RF Photonics
• Microwave Sensing Systems
• +5 more

Kamran Entesari is a Professor of Electrical and Computer Engineering at Texas A&M University, holding the Texas Instruments Engineering Professorship. His research focuses on RF/microwave/mm-wave integrated circuits, integrated RF photonics, and biochemical sensing systems. He leads projects in silicon photonics, millimeter-wave communication, and dielectric spectroscopy. Education: Ph.D., Electrical Engineering, University of Michigan, Ann Arbor (2005) M.S., Electrical Engineering, Tehran Polytechnic University (1999) B.S., Sharif University of Technology (1995) Research Interests: RFIC and mm-wave systems for 5G and beyond Photonic-integrated circuits for next-gen communication Biomedical sensing via microwave dielectric spectroscopy Low-power, high-linearity transceiver architectures His work bridges silicon photonics with traditional RF systems to enable high-speed, low-power solutions. Recent Research Trends: Recent publications emphasize hybrid CMOS-silicon photonics for mm-wave front-ends, full-duplex transceivers, and ultra-wideband sensing systems. Key innovations include reconfigurable photonic filters, beamforming networks, and interferometric sensors. Awards: IEEE Fellow (2025) Qualcomm Faculty Award (2017, 2018) NSF CAREER Award (2011) Outstanding Faculty Award (2012) Grants & Labs: Leads the SpecEES Initiative for energy-efficient mm-wave platforms. Collaborates with Texas Instruments on silicon photonics integration. His lab develops prototypes ranging from chip-scale sensors to phased array systems. Facility Affiliations: Based in the Wisenbaker Engineering Building (WEB 315C), part of Texas A&M's College of Engineering. Active in the Department's photonics and RFIC research clusters.

Mike BanadView profile

Associate Professor

• Machine Learning
• Neuromorphic Computing
• Energy-Efficient Hardware
• +3 more

Mike Banad is an Associate Professor at the University of Oklahoma's College of Engineering, Department of Electrical and Computer Engineering. He focuses on emerging machine learning applications and hardware implementation of energy-efficient neuromorphic computing. Current academic roles: Associate Professor, University of Oklahoma Prior roles: Researcher at Harvard University, Faculty at Southern University (Louisiana), R&D engineer at Samsung Electronics Research interests include secure and energy-efficient AI hardware, neural-inspired hardware acceleration, and co-design of algorithms and hardware using beyond CMOS technologies. He leads the Neuromorphic Intelligent Computing Systems (NICS) lab, which explores materials, devices, and systems for neuromorphic engineering. Prospective students are encouraged to apply by sending their CV and transcripts to his email address. Office: DEH 342. Research lab website: Neuromorphic Intelligent Computing Systems Lab .

Andreas G. AndreouView profile

Professor

• Computing Machinery
• Sensory Information Processing
• Theoretical Neuroscience
• +6 more

Andreas G. Andreou is a Professor at Johns Hopkins University with primary appointments in the Department of Electrical and Computer Engineering, and secondary appointments in the Department of Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the Johns Hopkins University Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab alongside Philippe Pouliquen. His research focuses on brain-inspired microsystems for sensory information processing, neuromorphic computing, and theoretical neuroscience. Education: Born in Nicosia, Cyprus; resides in Baltimore, Maryland. Research Interests: Computing Machinery, Sensory Information Processing, Theoretical Neuroscience, Pattern Analysis, Machine Intelligence, Microsystems Technologies, and Integrated Circuits. Awards: IEEE Fellow, 3rd Best Paper Award at IEEE BioCAS 2018, and recognition for the award-winning Stethovest wearable acoustic sensing array. His lab explores energy-efficient computing beyond Moore's Law, integrating neuroscience principles into microsystem design. Recent projects include quantum-inspired neuromorphic optimizers, AI-generated chips using ChatGPT4, and applications in cardiac acoustics and wearable technology. Collaborations span institutions like NSF, JHU-APL, and the Telluride Neuromorphic AI workshop. His work is supported by grants from DARPA, NSF, NIH, ONR, AFRL, and JHU-APL. He also holds honorary professorships at the University of Cyprus and Universidad Nacional del Sur.

Joachim KnochView profile

Professor

• Quantum technology
• Nanotechnology
• Nanoelectromechanical systems (NEMS)
• +7 more

Professor Joachim Knoch is a Full Professor and holds the Chair of Semiconductor Electronics at the Institute of Semiconductor Electronics, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany. He has been in this position since May 2011. Prior to this, he served as an Associate Professor at TU Dortmund University (2008–2011) and held research positions at IBM Zurich Research Laboratory and Forschungszentrum Jülich. He completed his PhD and Diploma in Physics at RWTH Aachen University and was a Postdoctoral Fellow at MIT. PhD in Physics, RWTH Aachen University (1998–2001) Diploma in Physics, RWTH Aachen University (1992–1998) Associate Student, Queen Mary University of London (1994–1995) His research interests are centered on the frontiers of nanoelectronics and quantum technologies. He investigates quantum technology, nanotechnology, nanoelectromechanical systems (NEMS), and neuromorphic hardware . His work explores novel transistor concepts, particularly focusing on cryogenic electronics, Schottky barrier transistors, and alternatives to conventional doping in nanoscale field-effect transistors using materials like SiO 2 and Si 3 N 4 . He also conducts significant research on spin qubits in silicon and the application of 2D materials such as graphene and transition metal dichalcogenides in electronic devices. The trends in his recent publications (2023–2024) highlight a strong and consistent focus on advancing cryogenic electronics for quantum computing applications, engineering novel semiconductor materials like GeSn nanowires, and developing innovative device concepts such as ultrasteep slope transistors and neuromorphic hardware. His work bridges fundamental physics, materials science, and practical electronic engineering, often involving detailed device modeling and multiscale simulation. His scientific achievements have been recognized with several awards: IBM Technical Accomplishment DFG Fellowship for Postdoctoral Research Borchers-Plakette for excellent PhD work, RWTH Aachen University Spingorum-Denkmünze, RWTH Aachen University Professor Knoch has actively supervised numerous doctoral students to completion and has been involved in significant collaborative research projects, often in partnership with institutions like Forschungszentrum Jülich and industry leaders. His research is supported by extensive publication output and leadership in the field of advanced semiconductor devices. He leads a research group that investigates the physics and fabrication of next-generation electronic components.

• Electronic Systems for Extreme Environments
• Quantum Physics Applications
• Power Electronics
• +5 more

Professor Alton Horsfall holds a position at Durham University's Department of Engineering. He earned his BSc (1993) and PhD (1997) in Physics from Durham before working at the Defence Evaluation and Research Agency. A former Reader at Newcastle University, he is now a Fellow of the Royal Academy of Engineering (since 2017). His research focuses on electronic systems for extreme environments, including nuclear reactors, jet engines, and volcanoes. Notable innovations include high-temperature power electronics operating beyond 300°C and quantum physics-based electric field mapping in semiconductors. He collaborates with institutions like Heriot Watt University and industry partners such as Rolls Royce and BAE Systems. Horsfall leads the development of Solid State Transformers and has pioneered nano-scale magnetometry for cell imaging. He has secured funding from EPSRC, Innovate UK, and major corporations, and sits on the EPSRC College and IEEE Sensors Conference Technical Programme Committee. With 27 successful PhD supervisions, he was honored as the Best Lecturer in Electrical Engineering (2014). Current projects include radiation-resistant amplifiers and high-temperature current monitors. Horsfall is Guest Editor for a special issue on Sensors for Extreme Environments in the Sensors Journal, highlighting his leadership in advancing frontier technologies.

• Machine Learning
• VLSI Design
• Communication Systems
• +5 more

Naresh R. Shanbag is the Jack S. Kilby Professor of Electrical and Computer Engineering at the University of Illinois, affiliated with the Coordinated Science Lab (414). His expertise spans energy-efficient machine learning systems, communication systems in scaled silicon and beyond CMOS technologies, VLSI architectures, and integrated circuit design. He holds a Ph.D. in Electrical Engineering from the University of Minnesota, awarded in June 1993. Dr. Shanbag has been recognized for his teaching excellence with multiple listings in the University of Illinois's 'Incomplete List of Teachers Ranked Excellent' across various academic terms: Fall 2020, 2019, 2018, 2012, and 2011. His research focuses on advancing energy and latency-efficient solutions in machine learning, communications, and signal processing, leveraging interdisciplinary innovations in systems, algorithms, and integrated circuits. Key honors and awards include the 2024 Semiconductor Innovation Award, the 2021 Neural Information Processing Systems Spotlight Paper, and the IEEE Fellow distinction in 2006. He has also received the D. J. Gandhi Distinguished Visiting Professorship from IIT Mumbai, India, and numerous best paper awards from prominent conferences like IEEE ISCAS and ISLPED. He actively mentors undergraduates in integrated circuit design, VLSI architectures for DSP/communications, and machine learning implementations. His research group innovates in deep in-memory architectures for edge inference, resource-constrained machine learning platforms, and VLSI circuits for autonomous systems and AI. He has authored/co-authored influential books, including 'Deep In-memory Architectures for Machine Learning' (2020) and 'Pipelined Adaptive Digital Filters' (1994).

C.Y. Roger ChenView profile

Professor

• VLSI Timing Analysis
• Power Leakage Reduction in CMOS Circuits
• Software Debugging and Verification
• +2 more

Roger Chen is a Professor at Syracuse University specializing in VLSI design, power leakage reduction, and software verification systems. His work bridges theoretical circuit analysis with practical emergency response infrastructure development. Education: Ph.D. in Electrical Engineering from University of Illinois at Urbana-Champaign (1987) Research Focus: Professor Chen pioneers transistor-level power management techniques including hybrid body bias methods for nano-scale circuits and transistor block reordering for idle-mode leakage reduction. His software debugging research introduces advanced program property querying beyond conventional IDE capabilities, while his distributed systems work develops collaborative platforms for emergency response scenarios like the IC.NET message routing system. Publication Trends: His 2007-2010 publications reveal a strategic research trajectory from fundamental VLSI timing analysis toward applied emergency management systems. The work demonstrates consistent innovation in low-power circuit design while expanding into critical infrastructure applications, showing exceptional versatility across semiconductor physics and distributed computing domains. Academic Leadership: Professor Chen actively mentors doctoral students in developing next-generation circuit simulators with near-SPICE accuracy and novel debugging environments. His research collaborations span transistor physics, software engineering, and emergency response protocols, reflecting interdisciplinary leadership in computer engineering.

• Neuromorphic Engineering
• Spiking Neural Networks
• Analog Integrated Circuits
• +3 more

Willian Soares Girão is a Researcher at the Faculty of Science and Engineering, University of Groningen, working within the Bio-inspired Circuits & Systems group. His research focuses on neuromorphic engineering and analog circuit design for neural networks, with particular emphasis on spiking neural networks and on-chip learning mechanisms for beyond-CMOS device integration. His primary research interests include neuromorphic computing, analog integrated circuits, and bio-inspired circuit design. He investigates synaptic models, attractor networks, and trace generation mechanisms to develop energy-efficient neuromorphic systems that mimic biological neural processing. His work bridges electrical engineering and computational neuroscience through hardware implementations of neural learning rules. Analysis of his 2023-2025 publications reveals a strong trend toward hardware-efficient neuromorphic processors with on-chip learning capabilities. His research spans synaptic normalization circuits, subthreshold integration techniques, and robust attractor networks for adaptive working memory applications. The work consistently integrates analog CMOS circuit design with biologically plausible learning rules while addressing device integration challenges for next-generation neuromorphic systems. Dr. Girão actively contributes to the Bio-inspired Circuits & Systems group at the University of Groningen, collaborating with international researchers on neuromorphic hardware development. His publications in ACM ICONS and IEEE ISCAS demonstrate technical expertise in analog circuit design for spiking neural networks, with multiple papers receiving significant citations and downloads in the neuromorphic computing community.

David V. PlantView profile

Professor

• Optical Communications
• Silicon Photonics
• Photonic Integrated Circuits
• +7 more

David V. Plant is a Professor in the Department of Electrical & Computer Engineering at McGill University , where he leads the Plant Group . His research spans optical communications , silicon photonics , and photonic integrated circuits , with a focus on high-speed data transmission systems for data centers and beyond. Research Interests: Optical Communications: Coherent and direct detection systems, high-capacity WDM links, quantum encryption. Silicon Photonics: SiP modulators, receivers, switches, and PICs for next-gen interconnects. Data Center Interconnects: Tbps-scale links using TFLN MZMs, QD-MLL combs, and CMOS drivers. Microwave Photonics: Reconfigurable filters, optoelectronic oscillators, and signal processors. Biomedical Optics: Seismocardiography signal generation and analysis using ML techniques. Scientific Awards: Lumentum Diversity, Inclusion, & Belonging Scholarship (industrial award) The Marconi Society’s 2023 Paul Baran Young Scholar (international award) Advanced Photonic Congress SPPCom Best Student Paper Presentation (international award) 2023 IEEE Photonics Society Graduate Student Fellowship (international award) 2023 SPIE Scholarship in Optics and Photonics (international award) Supervision & Grants: Prof. Plant has supervised numerous Ph.D., M.Eng., and post-doctoral researchers, including recipients of prestigious awards. His group collaborates with industry leaders like Ericsson, Ciena, and Lumentum, securing patents and advancing commercial applications in optical networking. Labs & Teams: The Plant Group operates within McGill’s ECE department, fostering a multidisciplinary environment for experimental and theoretical research in photonics. The team includes research professionals, visiting scholars, and administrative support from STARaCom.

Christof TeuscherView profile

Professor

• Non-classical computation
• Biomolecular computation
• Neuromorphic computation
• +7 more

Christof Teuscher is a Professor at Portland State University's Maseeh College of Engineering & Computer Science. His research focuses on developing disruptive computing paradigms through interdisciplinary approaches that merge computer science, physics, biology, and cognitive science. Education: PhD in Computer Science, Swiss Federal Institute of Technology Lausanne (EPFL), 2004 M.Sc. in Computer Science, EPFL, 2000 Research Interests: Non-classical computation Neuromorphic and thermodynamic computing Machine learning optimization Complex adaptive systems Reservoir computing in biological/chemical systems Recent Academic Contributions: 2025 course: Hardware for AI/ML (ECE 410/510) Labs developing reversible computing architectures NSF REU and altREU programs for undergraduates Artist-in-the-Lab (AiL) interdisciplinary program Advising & Lab Activities: Supervising PhD student Byron Gregg Published 300-page research book with students Presentations at IEEE ICRC and Green Computing conferences